Li et al. BMC Medical Genetics(2020) 21:208
https://doi.org/10.1186/s12881-020-01145-4

RESEARCH ARTICLE

Open Access

Development of an immunogenomic
landscape for the competing endogenous
RNAs network of peri-implantitis
Yang Li1,2†, Jina Zheng3†, Chanjuan Gong1, Kengfu Lan1, Yuqing Shen1* and Xiaojun Ding1,2*

Abstract
Background: Peri-implantitis is an inflammation that occurs around the implant, resulting in varying degrees ofinflammatory damage to the soft and hard tissues. The characteristic criterion is the loss of the supporting bone in
an inflammatory environment. However, the specific mechanisms and biomarkers involved in peri-implantitis
remain to be further studied. Recently, competing endogenous RNAs (ceRNA) and immune microenvironment havebeen found to play a more important role in the inflammatory process. In our study, we analyzed the expression of
immune related microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and message RNAs (mRNAs) in periimplantitis by analyzing GSE33774 and GSE57631.
Methods: In this study, we explored the expression profile data of immune-related lncRNAs, miRNAs and mRNAs,and constructed immune-related ceRNA network involved in the pathogenesis of peri-implantitis. In addition, the
CIBERSORT was used to evaluate the content of immune cells in normal tissues and peri-implantitis to detect theimmune microenvironment of peri-implantitis.
Results: In the analysis, 14 DElncRNAs, 16 DEmiRNAs, and 18 DEmRNAs were used to establish an immune related
ceRNA network and the immune infiltration patterns associated with peri-implantitis was discovered. Through themutual verification of the two datasets, we found that GSK3B and miR-1297 may have important significance in the
immune microenvironment and pathogenesis of peri-implantitis and GSK3B was closely related to four types of
immune cells, especially with the highest correlation with resting mast cells (P = 0.0003).
Conclusions: Through immune-related ceRNA network, immune-related genes (IRGs) and immune cell infiltration
can further comprehensively understand the pathogenesis of peri-implantitis, which built up an immunogenomic
landscape with clinical significance for peri-implantitis.
Keywords: Competing endogenous RNAs (ceRNA), Peri-implantitis, Immune microenvironment, GSK3B, miR-1297* Correspondence: shen.yuqing@zs-hospital.sh.cn;
dingxiaojun051214@163.com†
Yang Li and Jina Zheng are Co-first author.
1
Department of Stomatology, Zhongshan Hospital, Fudan University, No. 180
Fenglin Road, Xuhui District, Shanghai 200032, China
Full list of author information is available at the end of the article© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Li et al. BMC Medical Genetics(2020) 21:208

One-sentences summary
GSK3B and miR-1297 may have important significancein the immune microenvironment and pathogenesis ofperi-implantitis.
Background
With the advantages of stability, low foreign body sensation, non-injury to adjacent teeth and high masticatory
efficiency, dental implants have become an importantmeans to repair dentition defect or absence, and havebeen accepted by the general public. Peri-implant diseases include peri-implant mucositis and peri-implantitis[1]. Mucositis is defined as a bacteria-induced, reversible
inflammatory process of the peri-implant soft tissue withreddening, swelling and bleeding on periodontal probing,and peri-implantitis as a bacteria-induced, progressive
and irreversible disease of implant-surrounding hard andsoft tissues and is accompanied with bone resorption,
decreased osseointegration, increased pocket formationand purulence [2].
Currently, the incidence of peri-implantitis is high andcan lead to failure of the implant, wherein the plaque isthe initiating factor. There is no periodontal membranetissue support around the implant, and there are fewer
peripheral blood vessels and fibroblasts. Oral soft tissue
surrounds the perforated gingival part of the implant,
forming epithelial cuff of the implant, thus forming aprotective barrier around the implant. However, due tothe special structure of the epithelial cuff, its defense
ability weakened, and bacteria are easy to penetrate.
Once bacteria break through the epithelial cuff, they canquickly colonize the bone tissue, causing the damage tothe soft and hard tissue surrounding the implant, andforming inflammation of the tissue around the implant [3].
The immune response refers to the physiologicalprocess produced by the body’s immune system tostimulate antibody for the purpose of eliminating antigens. Peri-implantitis and periodontitis were similar in
the etiology and pathogenesis. Similar to periodontitis,
bacterial invasion of the surrounding tissue can trigger
an immune response. On the one hand, the reaction canremove harmful substances such as bacteria and toxins;on the other hand, the cytokines, proteases and prostaglandins produced in the process will accelerate thedestruction of the tissues around the implant [4]. With
the increasing popularity of dental implants, peri-implantitis
has attracted great attention, but its etiology and pathogenesis are still unclear. Immune response is the key to explore
the pathogenesis of peri-implantitis.
Competing endogenous RNAs (ceRNA) are a type of
non-coding RNAs that can bind miRNA response elements (MREs) to inhibit the formation of silencing complexes (miRISCs) and further increase the expression ofcorresponding mRNA to achieve mutual communication

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between RNAs and regulate gene expression after
transcription [5]. CeRNA mainly includes microRNAs(miRNAs), long noncoding RNAs (lncRNAs), circular
RNAs (circRNAs), pseudogenes, synthetic miRNA inhibitors and viral miRNA inhibitors. Among them, miRNAs
and lncRNAs are the most important types. LncRNAs,miRNAs, and mRNAs interact with each other to form
regulatory competitive endogenous RNA networks [6].
Recent studies have shown that there are significant
differences in the expression profile of miRNAs andlncRNAs between inflammatory and healthy gingival
tissue in patients with periodontitis [7]. However, theceRNA network involved in the pathogenesis of periimplantitis has not been reported by scholars. In thisstudy, we explored the expression profile data of
immune-related lncRNAs, miRNAs and mRNAs, andconstructed immune-related ceRNA network involvedin the pathogenesis of peri-implantitis, so as to further
discover the key immune-related genes (IRGs) in periimplantitis. In addition, we also studied the phenomenonof immune cell infiltration in peri-implantitis. Throughimmune-related ceRNA network, IRGs and immune cell
infiltration can further comprehensively understand thepathogenesis of peri-implantitis, which also provides a
new perspective for the diagnosis and treatment target of
peri-implantitis. In conclusion, our study has built up an
immunogenomic landscape with clinical significance for
peri-implantitis.

Methods
Datasets and data processing

The gene expression data of peri-implantitis patients from
GSE33774 (https://www.ncbi.nlm.nih.gov/geo/query/acc.
cgi?acc=GSE33774) were downloaded from the Gene
Expression Omnibus (GEO). In total, we downloaded theraw data of 7 peri-implantitis and 8 normal tissues foranalysis and used the RMA method to preprocess the original data. The IRGs were downloaded from the ImmPort
database (https://www.immport.org/), which provides anaccurate and up-to-date list of IRGs that participating in
immunization activities [8].

Differentially expressed genes analysis

The “Limma” package of R software (version: × 64 3.6.1)
were used to selected and differentially expressed lncRNAs(DElncRNAs) and differentially expressed genes (DEGs)
involved in peri-implantitis [9]. In addition, |log (FC)| > 1
and false discovery rate (FDR) < 0.05 were defined as thescreening criteria for DEGs and DElncRNAs. The expression of all IRGs were obtained from DEGs. Heatmaps of
IRGs and DElncRNAs were constructed, using the“Pheatmap” package.

Li et al. BMC Medical Genetics(2020) 21:208

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Exploration of the ceRNA network

Abundance calculations

The lncRNA-miRNA interactions were determined basedon data from the miRcode database (http://www.mircode.
org/) [10]. Then to predict the miRNA and mRNA interaction, the database of miRDB (http://mirdb.org/), TargetScan (http://www.targetscan.org/vert_72/) and miRTarBase(http://mirtarbase.mbc.nctu.edu.tw/php/index.php)
wereutilized. The obtained target genes were intersected with
IRGs [11–13]. The Cytoscape software 3.7.1 was used tovisualized the lncRNA-miRNA-mRNA network with theinteractions of results [14].

Immune cell abundance was calculated from the CIBERSORT results. CIBERSORT, a deconvolution algorithm
developed by Bindea G et al., can estimate the cell composition of complex tissues based on standardized gene
expression data, which can quantify the abundance ofspecific cell types [17]. CIBERSORT can enumerate 22
immune cell types at once and quantify the relative
proportions of each cell type using signatures from 500
marker genes.
Statistical analysis

Function and pathway enrichment

The Gene Ontology (GO) function and Kyoto Encyclopediaof Genes and Genomes (KEGG) pathway enrichment
analysis for the IRGs in ceRNA network were analyzed byusing the “clusterProfiler” package of R software, and P
value < 0.05 was considered statistically significant [15].
Validation of ceRNA network in another GEO dateset

In order to further verify the accuracy of immune-related
lncRNAs, miRNAs and mRNAs obtained, we verified itagain in another dataset. We downloaded the gene
expression of 2 normal tissue and 6 peri-implantitis from
GSE57631 (https://www.ncbi.nlm.nih.gov/geo/query/acc.
cgi?acc=GSE57631) and constructed the ceRNA network inthe same way. The results showed that only GSK3B andmiR-1297 existed in both ceRNA networks.
Validation of GSK3B by Weighted Gene Co-expression
Network Analysis (WGCNA)

Then we combined GSE33774 and GSE57631 and normalized them. Finally, we obtained the gene expression
profiles of 10 normal tissues and 13 peri-implantitis. In
order to validate GSK3B whether associated with periimplantitis, we selected the top 25% genes with thelargest variance as the input database and used the“WGCNA” package of R software (version: × 64 3.6.1)
to construct a weighted gene network. The standard
scale-free network is constructed by selecting the soft
threshold of the appropriate adjacency matrix. Next, wetransformed the adjacency matrix into topological overlap matrix (TOM), used hierarchical clustering to
generate the clustering gene dendrogram and set theminimum module size to 30. After determining thegene modules by dynamic tree cut method, we calculated the eigengenes of each module in turn, and thenconducted cluster analysis to merge the modules thatwere close to each other into a new module (height =0.25). The merged dynamic was the resulting modules.
Next, we analyzed the correlation between modules andtraits, selected modules containing GSK3B for further
analysis, and obtained gene significance (GS) and module membership (MM) [16].

In order to verify the relationship between the abundance of these 22 immune cells and the gene expressionof glycogen synthase kinase 3B (GSK3B), we calculated
the Pearson correlation between them, in which P valueless than 0.05 was significant.

Results
Identification of differentially expressed IRGs and lncRNAs
in peri-implantitis

Using a cut-of threshold of |log2 FC| > 1 and FDR < 0.05,
we identified 272 differentially expressed IRGs and 26
DElncRNAs. The heatmap of the DElncRNAs and differentially expressed IRGs showed that the peri-implantitis
clustered separately from the normal tissues (Fig. 1 andFigure S1).
Construction of a ceRNA network for peri-implantitis

We constructed and visualized the ceRNA network to better understand the role of lncRNAs on mRNAs mediatedby combination with miRNAs in peri-implantitis. Throughthe miRcode database, we retrieved 14 DElncRNAs interacting with 203 DEmiRNAs (Table S1). We then searchedfor differentially expressed mRNAs (DEmRNAs) from 203
DEmiRNAs in the miRDB, TargetScan and miRTarBase
databases. The DEmRNAs we obtained intersected withthe previous IRGs, and finally we obtained 14 DElncRNAs,
16 DEmiRNAs and 18 DEmRNAs interacting with eachother and established the corresponding ceRNA network(Fig. 2).
GO enrichment and KEGG signaling pathway analysis of
IRGs in ceRNA network

The GO enrichment analysis is divided into three parts:
biological process (BP), cell composition (CC) and molecular function (MF). After screening, it was found that
the IRGs in ceRNA network were enriched in transmembrane receptor protein kinase activity, protein tyrosine
kinase activity and growth factor binding, suggesting thatthese IRGs were enriched in inflammatory pathways(Fig. 3a). The KEGG analysis showed that the IRGs weremainly concentrated in human cytomegalovirus infection,

Li et al. BMC Medical Genetics(2020) 21:208

Fig. 1 Differentially expressed lncRNAs between peri-implantitis and normal tissues

Fig. 2 Map of the immune-related lncRNA-miRNA-mRNA network generated using Cytoscape 3.7.1 software

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Fig. 3 Functional enrichment of differentially expressed immune-related genes (IRGs). a The Gene Ontology (GO) annotations of differentiallyexpressed IRGs. b The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways

Li et al. BMC Medical Genetics(2020) 21:208

MAPK signaling pathway and PI3K-Akt signaling pathway(Fig. 3b).
Validation of gene expression of differentially expressed
IRGs

By constructing ceRNA network in another dataset
GSE57631 in the same way, we found that only GSK3B
and miR-1297 existed simultaneously in these two
networks. Then we further identified the immune gene
GSK3B using the other methods (Fig. 4).
Co-expression analysis by weighted gene co-expression
network analysis and identification of key modules

We combined GSE33774 and GSE57631 and normalized
them. Finally, we obtained the gene expression profiles of
10 normal tissues and 13 peri-implantitis. The WGCNA
analysis was designed to search for co-expression gene
modules and to explore the association between the gene
network and the phenotypes of concern. This time, we
downloaded the gene expression information and clinicalinformation of 10 normal tissues and 13 peri-implantitis(Fig. 5a). In this study, we chose the power of β = 32 toconstruct a scale-free network (Fig. 5b). Finally, after
removing the gray module, we obtained 7 gene coexpression modules by combining dynamic tree cutting(Fig. 5c). After combining the module with clinical traits,
we found that the brown module was closely related tothe disease states (P = 0.003), and it also contained theprognostic gene GSK3B (Fig. 5d). In addition, the cluster

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tree and eigengene adjacency heatmap between modules
were shown in Fig. 5e. Finally, we drew the scatter
diagram of GS vs MM in the brown module with the periimplantitis (correlation = 0.44, P = 1.4e-27) (Fig. 5f).
Immune cells in peri-implantitis

We predicted the abundance of immune cells in eachsample through CIBERSORT, and plotted the corresponding histogram, heatmap and box map of the content of
immune cells (Fig. 6a-c). From the figures, we could see
the obvious difference between the normal tissue andperi-implantitis. In addition, we also drew the heatmap of
the relationship between the corresponding 22 immune
cells (Fig. 6d).
Correlation analysis

We calculated the Pearson correlation to verify the relationship between the abundance of these 22 immune
cells and the gene expression of GSK3B. The results
showed that GSK3B was associated with four types of
immune cells: NK cells resting (correlation = − 0.5915,
P = 0.0428), T cells follicular helper (correlation = 0.6044,
P = 0.0374), Macrophages M1 (correlation = 0.614, P =0.0337) and mast cells resting (correlation = 0.8595, P =0.0003) (Fig. 7a-d).

Discussion
The application of implant denture is a breakthrough inthe field of stomatology in recent years. However, peri-

Fig. 4 Map of the immune-related lncRNA-miRNA-mRNA network in another dataset GSE57631

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Fig. 5 Weighted gene co-expression network construction and identification of the brown module containing GSK3B. a Clustering dendogram ofsamples. b The scale independence and the mean connectivity of the weighted gene co-expression network analysis (WGCNA) analysis of periimplantitis. Testing the scale free topology when β = 32. c Clustering dendrograms and modules identified by WGCNA. d Correlation between
module eigengenes and clinical traits. The brown module containing GSK3B is selected. e The cluster tree and eigengene adjacency heatmap
between modules. f The scatter diagram of gene significance (GS) vs module membership (MM) in the brown module of peri-implantitis. Gene
GSK3B has been highlighted in red dot, the cutoff of brown module membership = 0.88, the cutoff of gene significance for peri-implantitis = 0.35

implantitis can affect the long-term success rate ofdental implants, which is one of the main reasons forthe failure of dental implants. Peri-implantitis is achronic progressive marginal inflammation, which belongs to peri-implant tissue disease. Peri-implant disease
includes peri-implant mucositis and peri-implantitis.

The former is a reversible disease involving only soft
tissue. The latter is the result of further development ofmucositis, not only involving soft tissue, but also violated
the deep alveolar bone, resulting in bone resorption. If
peri-implantitis is not treated in time, it will lead tocontinuous bone resorption and implant-bone interface

Li et al. BMC Medical Genetics(2020) 21:208

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Fig. 6 Immune cell composition in peri-implantitis and normal tissues. a Composition of infiltrating immune cells in peri-implantitis and normal
tissues summarized from calculated mean values for each group. b The heatmap shows the differentially infiltrating immune cells between periimplantitis and normal tissues. c The fraction of infiltrating immune cells in peri-implantitis and normal tissues. d The correlation heatmap
between each immune cell

separation, which will eventually lead to implant loosening
and falling off [18]. The risk factors include periodontitis,
smoking, diabetes, poor oral hygiene, systemic disease,keratinized gingival width, IL-1 gene polymorphism, force
and implant site. Most scholars believe that bacterial infection is closely related to peri-implantitis [19]. Currently,
the treatment of peri-implantitis can be divided into
surgical and non-surgical treatment, in which surgicaltreatment can remove subgingival plaque and sediment,
and Guided Bone Regeneration (GBR) surgery can effectively solve the bone resorption problem. However, due tothe large surgical trauma, it is easy to cause gingival
retraction, and some scholars proposed that only in caseswith severe bone resorption and periodontal exploration
depth of more than 5 mm after conventional treatment[20]. The treatment of the peri-implantitis is a problem
in the current Implantology, and traditional debridement, scaling, anti-bacterial treatment effect is not verysatisfactory.
Similar to periodontitis, immune microenvironment
plays an important role in the pathogenesis of periimplantitis, so it is of great significance to understandthe immune response and immune microenvironment toclarify the pathogenesis of peri-implantitis and its clinicaltreatment. Berglundh et al. found that when the detection
rate of neutrophils and macrophages in the peri-implanttissues was relatively high in peri-implantitis. In the stateof inflammation, pathogenic bacteria invading the tissue
would promote the expression of the adhesion molecule
ICAM-1 in the peri-implant mucosa and the binding
epithelium, forming a certain chemotaxis gradient in thesurrounding tissue, which would induce a large number ofneutrophils to enter the gingival groove and engulf the invading pathogenic bacteria [21]. Liskmann et al. believedthat the secretion of IL-6 by activated Th1 cells played apromoting role, while the secretion of IL-10 by Th2 cells
had an anti-inflammatory effect, and the imbalance of thetwo roles would lead to intensified inflammatory responsein the surrounding tissues of implants and the destruction
and absorption of alveolar bone [22]. Lin et al. found that
activated CD4+ T cells could express the receptor activator of nuclear factor-κB ligand (RANKL), bind to thereceptor activator of nuclear factor κB (RANK) on thesurface of the osteoclast precursor cells, and promote theLi et al. BMC Medical Genetics(2020) 21:208

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Fig. 7 Relationships between the gene expression of GSK3B and infiltration abundance of four types of immune cells. a NK cells resting; b T cells
follicular helper; c Macrophages M1; d Mast cells resting

maturation, differentiation and bone absorption of osteoclasts [23]. Periodontal pathogens are the initiating factorsof peri-implantitis, and the tissue damage caused by the
immune response is often more serious than that causedby pathogens themselves. This suggests that in the treatment of peri-implantitis, one should focus on antibacterial, the other should focus on the regulation of immune
microenvironment of peri-implantitis, so as to improve
the treatment effect to some extent. Therefore, it isexpected to provide a new approach for the treatment ofperi-implantitis by exploring the role of immune microenvironment in the development of peri-implantitis andproposing more targeted diagnosis and immunotherapy.
CeRNA regulatory network is a newly discovered
mechanism for RNA interaction and regulation ofcoding genes, which extends the previous understandingof the large number of non-coding RNAs in vivo [24].
MiRNAs are newly discovered small molecules that play
an important role in gene expression regulation networks. Although miRNAs account for only a small partof the human genome, they are the key regulators ofbody development and cell homeostasis. MiRNAs arewidely expressed in higher eukaryotes and belong to aclass of non-coding small RNA molecules. In mammals,
miRNAs generally regulate gene expression at the translation level, and the number of miRNAs correlates withthe level of translational silencing at each site [25].
This study screened the differentially expressed lncRNAs
and IRGs between peri-implantitis and normal tissues andwe constructed the corresponding ceRNA network to
identify the key hub genes in peri-implantitis. In addition,
we conducted GO and KEGG enrichment analysis of IRGs
in ceRNA network, and we found that these differentiallyexpressed IRGs were mainly associated with inflammatory
processes. Then we verified the DElncRNAs, DEmiRNAs
and DEmRNAs obtained in another dataset and found that
only GSK3B and miR-1297 existed in both ceRNA networks. Next, we further verified the accuracy of the results.
We combined GSE33774 and GSE57631 and conducted
WGCNA analysis, which showed that GSK3B appeared in
key modules and had an important relationship with periimplantitis. In order to better detect the immune

Li et al. BMC Medical Genetics(2020) 21:208

microenvironment of peri-implantitis, the CIBERSORT wasused to evaluate the content of immune cells in normal tissues and peri-implantitis, and Pearson correlation was calculated between the gene expression level of GSK3B and 22
immune cells in peri-implantitis. The results showed that
GSK3B was closely related to four types of immune cells.
GSK3B is a serine/threonine kinase originally identified as a regulator of glycogen deposition, the role ofwhich in osteoblasts has been well demonstrated as anegative regulator of β-catenin. It has been reported toplay a role in regulating a variety of cellular responses,
including cell growth, migration, tumorigenesis andcytokine production [26]. Amirhosseini et al. reported
that inhibition of GSK3B could regulate osteoblast andosteoclast differentiation to suppress instable-induced
osteolysis and bone loss by activating Wnt/β-cateninsignaling [27]. In addition, Jang et al. demonstrated anovel role for GSK3B in regulating RANKL signaling,which was vital for osteoclast differentiation [28]. In thisstudy, it was found that GSK3B was associated with fourtypes of immune cells, especially with the highest correlation with resting mast cells (P = 0.0003). Madeleine
Radinger et al. determined that GSK3B was constitutively activated in resting human mast cells, which regulated by the phosphorylation status of activating tyrosine
residue Y216. In resting human mast cells, they observed
constitutive phosphorylation of Y216. However, when
the cells were activated, the phosphorylation of this residue have no consistent increase. They found that GSK3B
may have functions that precisely control the signaltransduction processes required for mast cell chemotaxis
and cytokine production [29]. In addition, they also
found a small molecule drug that inhibits GSK3B activity
could reduce the intensity of allergic inflammatory responses by reducing mast cell survival. They suggested
that GSK3B can be a key regulator of mast cell homeostasis through prevention of apoptosis [30]. With regard
to miR-1297, a growing number of studies have demonstrated that it may be associated with the pathogenesis
and prognosis of a variety of cancers. Bu et al. found thatmiR-1297 may act as an oncogene by regulating the
PTEN/Akt/Skp2 signaling pathway in non-small celllung cancer (NSCLC) cells [31]. Besides, Chen et al. confirmed that in human cervical cancer tissues, after theoverexpression of miR-1297, HeLa cells had an increasein cell proliferation and decrease in apoptosis. PTEN expression was negatively correlated with miR-1297 expression. PTEN silencing showed a similar pattern to theoverexpression of miR-1297, which inhibited the growth
and apoptosis of HeLa cells in vitro [32]. Through thesetwo ceRNA networks, we found that GSK3B and miR1297 are interconnected signal axis, and Gao et al. foundthat GSK3B was the downstream target gene of miR-1297
and growth arrest-specific 5 (GAS5) could be used as aPage 10 of 11

competing endogenous RNA for miR-1297 to weaken its
inhibitory effect on GSK3B [33].
This study has some limitations. First of all, this research mainly focuses on data mining and data analysis.
These results have not been verified by experiments, andwe will further verify the results of this study in the
future. In addition, we only obtained a limited datasetsand did not include more clinical status of each individual. More datasets will get a reliable result.

Conclusions
Through this study, we constructed and discovered theimmune related ceRNA network and immune infiltration
patterns associated with peri-implantitis, and further
explored the key genes that play a role in it. We found that
GSK3B and miR-1297 may have important significance in
the immune microenvironment and pathogenesis of periimplantitis, which needs to be further proved by later
experiments. Our study has built up an immunogenomic
landscape with clinical significance for peri-implantitis.
Supplementary information
The online version contains supplementary material available at https://doi.
org/10.1186/s12881-020-01145-4 .
Additional file 1: Table S1. 14 DElncRNAs interact with 203 DEmiRNAs
retrieved from the miRcode database. (DOC 672 kb)
Additional file 2: Figure S1. Differentially expressed immune-related
genes (IRGs) between peri-implantitis and normal tissues. (TIFF 2575 kb)
Abbreviations
CeRNA: Competing endogenous RNAs; MiRNAs: MicroRNAs; LncRNAs: Longnoncoding RNAs; MRNAs: Message RNAs; MREs: MiRNA response elements;
CircRNAs: Circular RNAs; IRGs: Immune-related genes; GEO: Gene Expression
Omnibus; DElncRNAs: Differentially expressed lncRNAs; DEGs: Differentiallyexpressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes
and Genomes; FDR: False discovery rate; WGCNA: Weighted gene coexpression network analysis; TOM: Topological overlap matrix; GS: Genesignificance; MM: Module membership; DEmRNAs: Differentially expressed
mRNAs; BP: Biological process; CC: Cell composition; MF: Molecular function;
GBR: Guided Bone Regeneration; RANKL: Receptor activator of nuclear factorκB ligand; RANK: Receptor activator of nuclear factor κB; GSK3B: Glycogensynthase kinase 3B; NSCLC: Non-small cell lung cancer; GAS5: Growth arrestspecific 5
Acknowledgements
Not applicable.
Authors’ contributions
YL, JNZ, YQS, XJD contributed to conception, design, data collection, analysis,
and interpretation, drafted and revised the manuscript; CJG contributed toconception, design, and data interpretation, drafted and revised themanuscript; KFL contributed to conception, design, and data interpretation,
critically revised the manuscript; All authors gave final approval and agreed
to be responsible for all aspects of the work.
Funding
The authors declare that there is no funding for the research.
Availability of data and materials
The datasets generated and analyzed during the current study are available
in NCBI GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33774
and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57631). The

Li et al. BMC Medical Genetics(2020) 21:208

IRG datasets are available in ImmPort database (https://www.immport.org/
shared/genelists). The lncRNA-miRNA interactions are available in miRcode
database (http://www.mircode.org/). The miRNA-mRNA interactions areavailable in miRDB (http://mirdb.org/), TargetScan (http://www.targetscan.
org/vert_72/) and miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.
php). The data of the DElncRNAs and differentially expressed IRGs in Fig. 1
and Figure S1 are available in GSE33774 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33774).
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Stomatology, Zhongshan Hospital, Fudan University, No. 180
Fenglin Road, Xuhui District, Shanghai 200032, China. 2State key laboratory ofmolecular engineering of polymers, Fudan University, Shanghai, P.R. China.
3
Department of Dermatology, Zhongshan Hospital, Fudan University,
Shanghai, P.R. China.
Received: 29 May 2020 Accepted: 12 October 2020

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